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Synthetic fields, real gains : enhancing smart sgriculture through hybrid datasets

  • Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.

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Author:Paul WachterORCiD, Niklas KruseORCiD, Julius SchöningORCiD
Title (English):Synthetic fields, real gains : enhancing smart sgriculture through hybrid datasets
Parent Title (German):Informatik in der Land-, Forst- und Ernährungswirtschaft - Fokus : Biodiversität fördern durch digitale Landwirtschaft
Publisher:Lecture Notes in Informatics (LNI), Gesellschaft für Informatik
Place of publication:Bonn
Document Type:Conference Proceeding
Year of Completion:2024
Creating Corporation:Gesellschaft für Informatik in der Land-, Forst- und Ernährungswirtschaft e.V.
Release Date:2024/03/07
Tag:Augmented data; Hybrid data; Reality gap; Smart farming; Synthetic data
First Page:437
Last Page:442
44. GIL-Jahrestagung, 27.02. - 28.02.2024, Stuttgart-Hohenheim (Deutschland)
Faculties:Fakultät IuI
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Review Status:Veröffentlichte Fassung/Verlagsversion
Licence (German):License LogoCreative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell - Weitergabe unter gleichen Bedingungen 4.0 International